Nathaniel Whittemore

As we discuss a lot at this show, we are somewhere in the three to four range right now.

Beyond that, there are a range of other definitions you might come across.

Stahl, we're told, Gardner defines AGI as, quote, the intelligence of a machine that can accomplish any intellectual task that a human can perform.

Google leans into a different aspect, describing AGI as hypothetical intelligence of a machine that possesses the ability to understand or learn any intellectual task that a human being can.

Amazon has another distinct focus, describing AGI as software that is, quote, able to perform tasks that it is not necessarily trained or developed for.

Now, if these are one-off definitions for blog posts, one of the more prominent attempts to define and test AGI capabilities is, of course, the Arc AGI Prize.

On their website, they write, "...the consensus definition of AGI, a system that can automate the majority of economically valuable work, while a useful goal, is an incorrect measure of intelligence."

Measuring task-specific skills is not a good proxy for intelligence.

Skill is heavily influenced by prior knowledge and experience.

Unlimited priors and unlimited training data allow developers to buy levels of skill for a system.

This masks the system's own generalization power.

Intelligence lies in broader general purpose abilities.

It is marked by skill acquisition and generalization rather than skill itself.

So they propose a better definition for AGI, is AGI is a system that can efficiently acquire new skills outside of its training data.

The ArcAGI test then seeks to test two elements of AGI contained in the definition.

The ability to acquire new skills by ensuring the tests have internal logic that can be learned, and the ability to complete tasks outside of training data by ensuring the tasks are not generally available.

So these are all the things that are floating around, and you can see while they broadly get us in the right category, there are a lot of different definitions which lead to a lot of debates and a lot of AGI is in the eye of the beholder kind of conversations, which, as I said, I don't think really matters for our day to day, but does matter when it comes to whether giant funds are going to press the sell button because they think things are overbought because we're not making enough progress towards AGI, which means all these contracts aren't going to play out the way that they want to.

So this is the context into which a group of researchers working with the Center for AI Safety have attempted to nail down a common definition and a metric for assessing models as they progress.

The group has produced a paper called Definition of AGI, which you can find at agidefinition.ai.

In the abstract, they write, the lack of a concrete definition for artificial general intelligence obscures the gap between today's specialized AI and human-level cognition.